Autonomous Vehicle Cloud-Service Testing for Proactive Issue Detection
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Solution Overview
Problem
Existing cloud services for autonomous vehicles often fail to identify a wide range of potential issues due to a reactive and iterative approach, leading to the neglect of numerous potential issues that may arise in the future.
Innovation Solution
A computing system simulates autonomous vehicles to test and measure cloud service behaviors by sweeping a range of inputs, adjusting variables, and verifying logic through simulated messages, implementing mitigations for unexpected behaviors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a reactive and iterative approach is used to test cloud services, then the testing process is simple to implement, but numerous potential issues are neglected and not identified
Solution Approach 1:
The system performs preliminary actions by proactively generating and executing test cases before actual cloud service operations occur. The automated testing framework pre-defines test scenarios, inputs, and expected outcomes, allowing potential issues to be identified before they manifest in production environments.
Solution Approach 2:
The system creates virtual copies of autonomous vehicles and cloud service environments to conduct tests without affecting real operations. These simulated environments replicate production conditions while allowing safe exploration of edge cases and failure scenarios that would be risky to test with actual vehicles.
2Reliability
If comprehensive testing with multiple variables is performed, then more potential issues are identified, but the testing process becomes more complex and time-consuming
Solution Approach 1:
The testing system operates continuously by executing test cases in automated sequences without interruption. The framework maintains continuous feedback loops where test results immediately trigger subsequent test cases or remediation actions, eliminating idle time between testing phases and maximizing productive testing throughput.
Solution Approach 2:
The system efficiently handles multiple variables by systematically changing test parameters across different test cases. The automated framework manages parameter combinations, input variations, and configuration changes programmatically, allowing comprehensive coverage of edge cases without manual intervention for each parameter adjustment.
3Reliability
If edge cases and unexpected behaviors are thoroughly tested, then cloud service robustness is improved, but the testing scope and complexity increase
Solution Approach 1:
The testing system segments the comprehensive test suite into modular, independent test cases that can be executed individually or in combinations. Each test case focuses on specific edge cases or unexpected behaviors, allowing the system to manage complexity through organized modules rather than monolithic testing procedures.
Solution Approach 2:
The automated testing framework acts as an intermediary layer between test designers and cloud service operations. This intermediary manages the complexity of coordinating multiple test variables, tracking results, and orchestrating remediation actions, shielding users from the underlying complexity while enabling thorough edge case testing.
Data Source
AI summary
Systems, methods, and apparatuses are provided for testing and measuring the behavior or logic of one or more cloud services associated with an updated AV bot script. For example, a system may be configured to receive a first autonomous vehicle (AV) bot script, reference data associated with a first cloud service, and configuration data identifying at least a first variable of a set of adjustable variables of a simulated AV, a first value of a set of values associated with the first variable, and the first cloud service. Additionally, the system may be configured to generate a second AV bot script based on the configuration data and the first AV bot script. Further, the system may be configured to receive cloud output data associated with the first cloud service and determine one or more differences between the cloud output data and the reference data.